Learning from a limited number of training samples has drawn increasing attention due to the high cost of collecting and annotating a large amount of data. Researchers have developed algorithms to improve the performance of models that have been trained with very few data.
[1]}, [2]} train models in a meta-learning fashion so that the model can adapt quickly on tasks with only a few training samples available.
[3]}, [4]} try to synthesize data or features by learning a generative model to alleviate the data insufficiency problem.
[5]} propose to leverage unlabeled data and predict pseudo labels to improve the performance of few-shot learning.
